The experiment reported in this article provides a first experimental evaluation of human–machine cooperation on decision level: It explicitly focuses on the interaction of human and machine in cooperative decision-making situations for which a suitable experimental design is introduced. Furthermore, it challenges conventional leader–follower approaches by comparing them to newly proposed automation designs based on cooperative decision-making models. These models originate from negotiation theory and game theory and allow for an investigation of cooperative decision making between equal partners. This equality is motivated by similar approaches on the action level of human–machine cooperation. The experiment's results indicate an added value of the proposed automation designs in terms of objective cooperative performance as well as human trust in and satisfaction with the cooperation. Hence, the experiment yields the same insight on decision level as already observed on action level: It may be beneficial to design machines as equal cooperation partners and in accordance to models of emancipated human–machine cooperation.

This article presents a systematic method for the design of a limited information shared control (LISC). LISC is used in applications where not all system states or references trajectories are measurable by the automation. Typical examples are partially human controlled systems, in which some subsystems are fully controlled by the automation, whereas others are controlled by a human. The proposed systematic design uses a novel class of games to model human–machine interaction: the near potential differential games (NPDG). We provide a necessary and sufficient condition for the existence of an NPDG and derive an algorithm for finding a NPDG, which completely describes a given differential game. The proposed design method is applied to the control of a large vehicle manipulator system, in which the manipulator is controlled by the human operator and the vehicle is fully automated. The suitability of the NPDG modeling differential games is verified in simulations leading to a faster and more accurate controller design compared with manual tuning. Furthermore, the overall design process is validated in a study with 16 test subjects indicating the applicability of the proposed concept in real applications.

This paper addresses the problem of transfer of control authority between a robot’s AI and a remote human operator, when controlling a Mixed-Initiative (MI) robotic system. We propose a negotiation-theoretic method that enables the robot’s AI and the human operator to cooperatively and dynamically determine (i. e. negotiate) the transfer of control authority between these two agents. An experimental study is presented in which a state-of-the-art Expert-guided Mixed-Initiative Control Switcher (EMICS) method is compared with our proposed Negotiation-Enabled Mixed-Initiative Control Switcher (NEMICS) algorithm. Results suggest that the NEMICS framework is able to successfully avoid conflicts for control, which is a fundamental challenge encountered with previous MI control methods. Comparing NEMICS with the EMICS, we provide evidence of improved navigational safety (i. e. fewer collisions). Additionally, our usability study suggests that human operators perceived their interactions with NEMICS as less intrusive than with EMICS.

The coordination of heterogeneous robotic teams demands suitable planning algorithms based on an appropriate model of the problem instance. While there exists a great variety of automated planning algorithms, the modeling of a problem instance often requires expertise in problem recognition and faculty of abstraction—a task which can be done best by humans. In order to exploit the synergy potential inherent in human-machine cooperative planning, we propose a new reoptimization framework based on a genetic algorithm (GA) for heterogeneous multi-robot task allocation problems including cooperative tasks and precedence constraints. The main idea of the reoptimization framework is to reuse insights from previous solutions of similar problem instances. In particular, a modified problem instance, resulting for example from adding or deleting individual tasks, is solved based on the solution of the unmodified problem instance. To this end, we introduce suitable heuristics for the adaption of the initial solution based on the considered problem modification. The simulative investigation of the proposed approach shows great positive effects compared to the application of a standardized GA that does not make use of the solution of the unmodified problem instance.

Large vehicle-manipulators are systems consisting of a medium-sized heavy-duty vehicle and a hydraulic manipulator. They operate in an unstructured environment and are therefore not fully automated. However, semi-automation of such a system is possible, where the automation controls the vehicle and a human operator controls the manipulator. Since in the unstructured environment not all system trajectories are measurable for the automation, a so-called Limited Information Cooperative Shared-Controller (LICSC) has been proposed in previous work. However, the design of the LICSC is done through tuning which is sensitive to the operator controlling the manipulator. The first contribution of this work is to reduce this sensitivity. A novel personalized design of the LICSC is presented that provides control tailored to the human operator. Our proposed approach uses state-of-the-art design methods of a cooperative controller to obtain the LICSC. The second contribution is the experimental validation of the LICSC, which proves the importance of the personalization to the human operator and demonstrates the advantage of the method.

In order to fully exploit the advantages inherent to cooperating heterogeneous multi-robot teams, sophisticated coordination algorithms are essential. Time-extended multi-robot task allocation approaches assign and schedule a set of tasks to a group of robots such that certain objectives are optimized and operational constraints are met. This is particularly challenging if cooperative tasks, i.e. tasks that require two or more robots to work directly together, are considered. In this paper, we present an easy-to-implement criterion to validate the feasibility, i.e. executability, of solutions to time-extended multi-robot task allocation problems with cross schedule dependencies arising from the consideration of cooperative tasks and precedence constraints. Using the introduced feasibility criterion, we propose a local improvement heuristic based on a neighborhood operator for the problem class under consideration. The initial solution is obtained by a greedy constructive heuristic. Both methods use a generalized cost structure and are therefore able to handle various objective function instances. We evaluate the proposed approach using test scenarios of different problem sizes, all comprising the complexity aspects of the regarded problem. The simulation results illustrate the improvement potential arising from the application of the local improvement heuristic.

In order to support a human in a human-machine system, the cooperating automation requires information about the goal pursued by the human. We model human-machine systems as a Partially Observable Markov Decision Process to develop an assistant system operating on maneuver or navigation level featuring an automatic detection of the humans goal which is initially unknown to it. Predicting the next actions of the human allows for computing corresponding assistant actions by employing Partially Observable Monte-Carlo Planning With Observation Widening. These supporting actions are generated based on continuous action-, observation- and state spaces and are executed synchronously to the actions of the human. New information about the humans goal is gathered through observations to further enhance future supporting actions. With a progressing certainty of the goal recognition, the assistant system is increasingly able to assist the human by completing the task both cooperatively or even fully autonomously, thus reducing workload, while always allowing for the human to smoothly in-or decrease their involvement. The assistant system demonstrates its ability to recognize the goal pursued by the human as well as to execute appropriate supporting actions while being robust to the human changing their goal in multiple experiments investigating the interaction of a real human with a simulated cooperative positioning scenario.

In the past decade, remarkable research has been done on human-machine cooperation to generate synergies and mutual benefits. However, most research so far only considers the control level of interaction with concepts like haptic shared control. This paper focuses on the emerging research on human-machine cooperation on higher levels of interaction to tackle more complex challenges. Therefore, we first introduce a generalized level model based on established models to define our research emphasis on emancipated human-machine cooperation on all levels. Second, the design and results of a study on human-machine cooperation on decision level are presented. We examine the negotiation behavior of humans in a scenario with discrete decision options and a deadline. The results indicate the validity of a previously proposed model based on negotiation theory to describe the observed human behavior. Additionally, the observed influencing factors on the negotiation behavior are crucial for a proper automation design: adaptation and identification methods are required to enable the automation to take part in an emancipated negotiation with a human.

Haptic shared control systems represent a useful approach for a safer and more intuitive cooperation between human and machines. For an adequate controller design, it is essential to understand the control behavior of a human during the interaction with a cooperation partner. A considerable amount of studies has shown that human behavior in a cooperative scenario is different than in a manual control task, indicating the need for modeling approaches which take the interaction into account. In this paper, we validate an approach to model haptic cooperative behavior of humans based on differential games, where the motion trajectories of each partner arise from the minimization of an individual cost function. We evaluate the applicability of the model for goal-oriented movements by means of an experiment consisting of 26 pairs of subjects moving a virtual marker cooperatively to a goal position. The interaction takes place through haptically coupled steering wheels. Moreover, we compare the differential game approach with another shared control model which considers the action of the cooperating partner as a system disturbance. The results show that the differential game model slightly outperforms the disturbance model in the approximation of observed motion trajectories with statistical significance.

Shared control systems have a great potential to contribute to a safer human-machine interaction. A great body of literature has been concerned with the design of the automation the human is sharing control with. At the same time, an adequate design is connected to the availability of reliable models of human behavior. A promising modeling approach is given by optimal control theory, where human behavior arises from the minimization of a cost function. However, most of the work found in literature focus on determining the cost function of the human in a situation without any haptic interaction with a partner, i.e. in manual control tasks. Motivated by several studies which indicate that human behavior changes when completing a task cooperatively, this paper proposes an optimal control approach for human behavior modeling in a shared control scenario. We further hypothesize that the human cost functions in a shared control scenario change significantly when compared to the ones which arise from a human performing a control task alone. We apply an inverse optimization approach in order to identify the cost function in both scenarios. In order to evaluate our hypothesis, a study was conducted where 42 participants performed a tracking task in a manual mode and then sharing control with an assistance system. The findings show that the model is able to describe human behavior in both shared and manual control. Furthermore, the results confirm that the human cost function changes considerably between both scenarios.

Shared control techniques have a great potential to create synergies in human-machine interaction for efficient and safe applications. However, an optimal interaction requires the machine to consider the individual behavior of the human partner. A widespread approach for modeling human behavior is given by optimal control theory, where the movement trajectories of a human arise from an optimized cost function. The aim of the identification is thus to determine parameters of a cost function which explains observed human motion. The central thesis of this paper is that individual cost function parameters which describe specific behavior can be determined by means of Inverse Reinforcement Learning. We show the applicability of the approach with a tracking control task example. The experiment consists in following a reference trajectory by means of a steering wheel. The study confirms that optimal control is suitable for modeling individual human behavior and demonstrates the suitability of Inverse Reinforcement Learning in order to determine the cost function parameters which explain measured data.

Shared control is a promising approach for designing an Advanced Driver Assistance System, since it unifies the advantages of both manual control and full automation. However, for a true cooperative shared control ADAS the automation has to understand the human and thus a suitable model which describes the driver in the control loop is essential. Our gray-box approach bases on the biological concept that humans realize motion by combining a finite set of motion primitives (we call movemes). With the assumption that a driver switches between movemes based on perceived information, we propose a Hidden Markov Model which determines the probability of each movement given a certain driving situation. Car turn maneuver experiments show a good approximation of steering trajectories recorded in a driving simulator. A comparison with a black-box model show that the movement-based driver model performs significantly better. In addition, training algorithms are available and the probabilistic approach of the model allows further interpretation of the results.